Smoke detection

ABSTRACT

A smoke detection system comprising video camera monitoring means, video frame comparison means, signal processing means, and alarm generating means dependant on an output from the signal processing means; the signal processing means being arranged to analyse successive frames acquired by the video camera monitoring means and to compare the intensity and/or colour of individual pixels or group of pixels so as to consider the overall characteristics and inter-relationships of these pixels so as to detect the presence of smoke characterised in that the signal processing means analyses a plurality of different types of change in the said characteristics and inter-relationships.

[0001] The present invention relates to smoke detection, and moreparticularly to smoke detection systems based on an analysis of videoimages.

[0002] It is sometimes necessary to monitor the environment of high riskequipment to ensure that dangerous potential fire hazards can berecognised at an early stage. Examples of areas where such monitoring isnecessary include power station turbine halls and enclosed high riskregions such as traffic tunnels.

[0003] Continuous monitoring by operators viewing on closed circuittelevision screens is unsatisfactory since the image to be viewed is notconducive of a continuous watch since the image itself is uninterestingto an observer and only small changes to an image need to be detected.Surveillance by electronic means is therefore desirable.

[0004] A previous approach to this monitoring problem had been to use avideo camera to monitor an area and then to identify when, through thepresence of smoke, the pixel values of the picture monitored by thetelevision camera became representative of a lighter image. The problemwith this system was that it could not detect smoke against a lightbackground or indeed fires which produce little smoke such as thoseemanating from propane.

[0005] Another problem is that of preventing false alarms caused bylighting changes such as people moving across the field of view.Previously that problem had been dealt with by examining an outer area,and then if changes occurred in that area, inhibiting the monitoringprocess in an inner region. This could result in delay in detection andalso mean that only sources of smoke in the inner region be detected.

[0006] The present invention is concerned with provision of a smokedetection system which overcomes these problems and enables a potentialfire risk situation to be recognised at an early stage.

[0007] Accordingly the present invention provides a smoke detectionsystem comprising video camera monitoring means, video frame comparisonmeans, signal processing means, and alarm generating means dependant onan output from the signal processing means; the signal processing meansbeing arranged to analyse different frames acquired by the video cameramonitoring means and to compare the intensity and/or colour ofindividual pixels or group of pixels so as to consider the overallcharacteristics and inter-relationships of these pixels so as to detectthe presence of smoke characterised in that the signal processing meansanalyses a plurality of different types of change in the saidcharacteristics and inter-relationships.

[0008] Generally the signal processing means will provide one frame(often, but not necessarily always, the immediately preceding frame) asa reference and compare the current frame with that reference. Thesignal processing means will compare, within a selected region of thatframe, the manner in which pixels or groups of pixels have changed.

[0009] There are a series of different kinds of change which can beanalysed and the following is a list of some of these:

[0010] an overall gate as a starting point to determine whethersignificant change has taken place

[0011] value of pixels identified as converging towards a mean value. Ineffect this means that extremes of contrast are falling and the image isbecoming more grey.

[0012] edge information changes. Edge information can be obtained bycomparing two frames of which one has been shrunk by a few pixelsrelative to the other.

[0013] The edge information can be used in two ways to define anemerging smoke condition, the one being to ascertain when edge detail isbeing lost, and the other being to distinguish between a softly definedimage such as a smoke cloud and a harder image such as a moving person.

[0014] dynamic parts of the overall image are identified as becomingstatic, or conversely static parts of the image become more dynamic.

[0015] compactness. This looks at the size and placement of measureddifferences in pixel content. If the changed pixels are distributed insmall isolated groups this will be indicative of an emerging smokecondition.

[0016] opacity. Opacity is calculated by comparing the value (brightnessor intensity) of the changed pixels to those in the reference image. Itlooks for a difference to indicate a reduction in visibility and hencethe emergence of smoke.

[0017] shape. Characteristics of shape which are known to berepresentative of an emerging smoke or fire condition.

[0018] Generally at least three, preferably the first, second and thirdchanges referred to above, should be analysed although othercombinations of the types of changes can be employed.

[0019] In practice a rule based analysis will be used initially based onthree or four of the different kinds of change, and if this indicates apotential smoke condition then statistical analysis will be carried out,for example by means of a Bayesian Analysis. The statistical analysiswill generally be based on all measured changes while the rule basedanalysis may trigger a smoke condition based on only a few of themeasured kinds of difference.

[0020] The rule based analysis may be operated by weighting eachselected individual analysis and summing the result, for example using apoint count system (the individual values of which are ascertainedempirically) to provide a pass/fail form of scoring.

[0021] Generally a selected zone of the overall image will be analysed,and this may be selected to exclude non-typical regions of the screenarea such as where personnel movement or other expected variation islikely to occur. Alternatively the whole area of the screen image may beanalysed.

[0022] An embodiment of the invention will now be described by way ofexample with reference to the accompanying diagrammatic drawings inwhich:

[0023]FIG. 1 is a schematic arrangement of an apparatus according to theinvention.

[0024] Referring to FIG. 1, an incoming signal enters a frame grabberfrom the output of one or more closed circuit video cameras and theoutput of the frame grabber goes firstly to a memory which retains eachsingle frame image in turn and then it goes to a signal processor whichincludes a comparator for the analysis of differences between differentframes and the pixels contained within those frames.

[0025] In this respect the comparator firstly compares the image withprevious images and by subtraction obtains a signal representative ofthe difference between successive frames. The system also includes anadjustable threshold control level, for sensitivity setting and a meansby which changes which are representative of signal noise can beeliminated.

[0026] The output of the comparator is then subjected to the mainprocessing of the signal in accordance with the present invention.Essentially the signal processor is looking to see whether there arechanges in the individual pixels of a frame and in the differencesbetween adjacent pixels which would have been caused by smoke particles.Thus if such dangerous changes have arisen an alarm is set off. Inparallel with this, a monitor screen shows the region being monitoredand can have an enhanced image superimposed on the raw TV camera imageproduced, so that the danger area is emphasised to an observer.

[0027] Now the signal processor involves a number of separate analysesand these involve mathematical analysis by appropriate computer softwarein the signal process as part of the equipment.

[0028] The signal processing means has to include hardware and/orsoftware to recognise the selected conditions of change so that thepresence of a smoke condition can be identified.

[0029] The analysis can be based on the following:

[0030] Notation and concepts

[0031] The system has two images to work with, where image is defined asan ordered set of pixels intensities.

[0032] First it is necessary to define the set of possible pixelintensity values

Z=<0, 1, 2, 3, . . . , M>

[0033] where M is the maximum pixel value.

[0034] An image is defined as an ordered set of pixel values where apixel value is defined as:

i_(j)εZ

[0035] Therefore an image can be denoted as follows

I=<i₀, i₁, i₂, i₃, i₄, . . . i_(N)>

[0036] Where N is the number of pixels in an image.

[0037] The system provides two images in order to evaluate the variouschanges. These images are

[0038] R the reference image

[0039] C the current image

[0040] Given that a change has been identified this change is used todefine a sub-set of the images.

R₆₆

R

C₆₆

C

[0041] With these sub-sets defined, the following metrics areevalulated:

[0042] Convergence to a Common Mean

[0043] There is the reference image R and the current image C. The setof pixels which are deemed to have changed are denoted C_(Δ)and R₆₆.

[0044] Let m be the mean value of the changes in C i.e.$m = {\frac{1}{\# C_{\Delta}}{\sum C_{\Delta}}}$

[0045] where

[0046] #S denotes the number of element in the ordered set S and

[0047] ΣS denotes the sum of all elements in the ordered set S.

[0048] Once the value m has been defined the number of pixels whoseintensity is approaching m with respect their corresponding value in thereference image is evaluated. With the same images the number of pixelswhose intensities are departing from this mean value is also calculated.

^(n)towards=Σ{sign(C _(Δ) −R _(Δ))=sign(C _(Δ) −m)}

^(n)away=Σ{sign(C _(Δ) −R _(Δ))≠sign(C ₆₆ −m)}

[0049] where the function sign is defined as follows for scalar values,when applied to an ordered set it is$\left. {{sign}(x)}\rightarrow\begin{matrix}{{x < 0}:{- 1}} \\{x = {0:0}} \\{{x > 0}:1}\end{matrix} \right.$

[0050] defined to return an ordered set of values.

[0051] These two values provide a metric of “convergence to the commonmean value” and passed forward to the decision system.

[0052] Static Becomes Dynamic

[0053] For any area which is being investigated, the consistency of thechanging area is evaluated over time in order to assess if that area isdynamic in terms of its overall appearance or static. Lighting changesalter the image but the overall appearance does not change. Thecorrelation function is used to evaluate this similarity over time sinceit is invariant to both scale and gain changes. If an object obscuresthe background by moving into the area of interest then the appearancewithin the area of interest will change. If the correlation fluctuatessufficiently over time then the area is deemed to be dynamic. Thismeasure of consistency is forwarded to the decision system.

[0054] Edge Content

[0055] A change in edge information is defined as a change in the valueof the edge measure. The edge measure is defined as the sum of theresponses of a standard derivative filter kernel where changes have beendetected by the previous stage. A standard filter which is employed isthe Sobel edge filter. This measure of edge content is forwarded to thedecision system.

[0056] Characteristics of Shape

[0057] Various shape characteristics are employed including density andaspect ratio.

[0058] Density is defined as the average number of occupied neighboursfor all members of the change set. A “four connectivity” scheme isadapted and consequently the average value of density lies in the range0 to 4.

[0059] The aspect ratio is the ratio of the height to the width of thechanged region.

[0060] When the density, aspect ratio and pixel count (i.e. the numberof pixels that have changed in an area) are taken together they describesome of the shape characteristics of the changed area. These values areforwarded to the decision system.

[0061] System Description

[0062] The smoke detection hardware accepts video input on up to sixparallel video streams. The hardware consists of an industrial PC with a200 MHz MMX Pentium processor and 32 MB of system RAM. The PC contains aframe-grabber card, of type MP300. The smoke detection software iswritten in C++, compiled using the WATCOM C++ compiler. The featuresdescribed below in this invention are encapsulated in around 50 sourcecode files and a further 50 header files, comprising an estimated 40,000lines of code in all.

[0063] Algorithm Overview

[0064] The smoke detection algorithm examines, in general terms, thefollowing features of a digitised video stream to determine whethersmoke has been detected:

[0065] Pixels (or groups of pixels) moving towards a mean value

[0066] Edge information edge definition—this may increase or decrease assmoke emerges (depending on what it was like before)

[0067] Whether the image overall is static or dynamic

[0068] Emerging new shapes in the image—comparison of characteristicshape with indicative smoke shapes

[0069] The system works out the differences between the current imageand a reference image. Important parts of the analysis are as follows:

[0070] Where image pixels appear to have changed, the algorithms workout whether the image pixels are approaching or deviating from somecommon mean value

[0071] Edges—sum of responses of a standard deviation filter wherechanges were previously detected (the Sobel edge filter)

[0072] Correlation function to determine similarity over time.

[0073] Shape: density of the “changed” region—four nearest neighbourspossible; aspect ratio; total area

[0074] Zones

[0075] Zones are rectangular regions selected from the entire image bythe user when the system is installed. These would typically be arrangedto cover likely areas where smoke might be produced, and (moreimportantly) not cover problem areas of the scene. Each zone isprocessed entirely separately, and the outputs from each zone may becombined to generate alarms as required. Pixels in the zone mayadditionally be eliminated so that they are not included in thecalculations—for example, the filament of a light bulb, or a shiny metalobject that glints in the sunlight. Again, these are selected by theuser when the system is commissioned. At any one time there are twoprimary sets of image data for the zone—the current image and thereference image. The pixels in these images are denoted by x and x_(r)respectively, in the discussions below.

[0076] Within each zone, a set of n parameters are calculated. Theseparameters are formed into an n-dimensional “vector”, defining a“feature” space.

[0077] Image Data (Planes) Stored in the Program

[0078] The following key image plane sets are stored by the software foreach zone:

[0079] current image data

[0080] reference reference image data

[0081] change raw changed pixels

[0082] environment edge-sensitive detector values from reference imagedata

[0083] filter the combined “mask”

[0084] previous previous value of “filter”

[0085] eliminate mask of pixels eliminated manually

[0086] Basic Operation

[0087] Frames are acquired from the frame grabber card on a regularbasis. After any adjustments to normalise the brightness and contrast,the system compares the most recently acquired frame (current) with thereference frame. If pixels differ by more than an adjustable threshold(camera noise may be taken into account too), then the pixel is deemedto have changed.

[0088] The reference image is acquired periodically, when the system hasdetected no changes in the scene, and when the system determines thatthe current scene is no longer similar enough to the reference image.This reference image is analysed to generate an “environment mask”,using the EDGE algorithm below. This essentially indicates where thereis edge detail in the zone.

[0089] A pixel-by-pixel “filter” mask, used in the calculations detailedbelow, is constructed by combining the changed pixels with theenvironment mask. The changed pixel mask is only copied to the finalfilter mask at points where the magnitude of the difference between thecurrent and the reference pixel exceeds the edge detail pixel value.Pixels selected manually as being problematic are also eliminated fromthis mask at this stage.

[0090] Low-Level Image Processing Operations

[0091] A large set of different image processing operations are carriedout on the zone image data. Some of these operations use only theunmasked pixels, others operate on the entire set of pixels. Theseparameters are the raw data fed into the final smoke detectionalgorithms. They are all relatively straightforward image processingprimitives, but the definitions used in the algorithm are reproducedbelow for completeness.

[0092] MEAN

[0093] This is the simple mean value of the N pixel values, x, in thezone. ${MEAN} = {{\langle x\rangle} = \frac{\sum x}{N}}$

[0094] TOWARDS_COMMON_MEAN

[0095] This parameter counts the number of unmasked pixels in the imagethat deviate from the mean with the same sign as they do in thereference image.

TOWARDS=Σ[sign(x−x _(r))=sign(<x>−x _(r))]

[0096] FROM_COMMON_MEAN

[0097] This parameter counts the number of unmasked pixels in the imagethat deviate from the mean with the opposite sign from the way they doin the reference image.

FROM=Σ[sign(x−x _(r))=sign(<x>−x _(r))]

[0098] COFGX

[0099] The mean x-co-ordinate of the unmasked pixels in the zone (thiswill change as areas in the zone are masked out)

[0100] COFGY

[0101] The mean y-co-ordinate of the unmasked pixels in the zone (thiswill change as areas in the zone are masked out)

[0102] SIZE

[0103] The total number of pixels in the zone, including the maskedpixels

[0104] COUNT

[0105] The total number of unmasked pixels in the zone (i.e. excludingthe masked pixels)

[0106] EDGE

[0107] The edge content algorithm looks at, for each unmasked pixel inthe current image, the four adjacent pixels (up/down/left/right). Itsums the sum of the magnitude of the differences between the left andright, and between the up and down pixels, for pixels where this exceedsa threshold value set by the user.

EDGE=Σ[{|x _(up) −x _(down) |+|x _(left) −x _(right)|}(if>threshold)]

[0108] EDGE_REF

[0109] The calculates the EDGE function, but based on the referenceimage pixels, instead of the current image pixels

[0110] CORRELATION

[0111] This is the correlation between the reference and the currentimage. This is calculated as:${CORR} = \frac{\left( {{N*{\sum{xx}_{r}}} - {\sum{x{\sum x_{r}}}}} \right)}{\sqrt{\left( {{N*{\sum x^{2}}} - \left( {\sum x} \right)^{2}} \right) \times \left( {{N*{\sum x_{r}^{2}}} - \left( {\sum x_{r}} \right)^{2}} \right)}}$

[0112] The correlation function is used as an overall “gate” to thedetection process. If this correlation is greater than a presetSIMILARITY, then no further processing is carried out on the zone. Thiscorresponds to the case where the image is essentially the same as thereference image.

[0113] CORRELATION_MASKED

[0114] The masked correlation calculates the same function as thecorrelation function above, considering only those pixels that are notmasked.

[0115] VARIANCE

[0116] This is the standard variance of the pixel value, x, includingall the pixels, calculated as${VAR} = {{{\langle x^{2}\rangle} - {\langle x\rangle}^{2}} = {\frac{\sum x^{2}}{N} - \left( \frac{\sum x}{N} \right)^{2}}}$

[0117] VARIANCE_REF

[0118] This is the standard variance of the reference pixel values,x_(r), including all the pixels, calculated as${VAR} = {{{\langle x_{r}^{2}\rangle} - {\langle x_{r}\rangle}^{2^{\prime}}} = {\frac{\sum x_{r}^{2}}{N} - \left( \frac{\sum x_{r}}{N} \right)^{2}}}$

[0119] SKEW, KURTOSIS and FIFTH

[0120] These parameters look at the distribution of all the pixel valuesin the current image. As an example, the pixel values might have aGaussian distribution about the mean pixel value, or the distributionmight be asymmetric or otherwise non-Gaussian. Parameters such as skew,kurtosis and fifth are well known parameters used in statistics toanalyse the non-Gaussian nature of distributions. They are calculated asfollows:${{Denoting}\quad \sigma} = \sqrt{{\langle x\rangle}^{2} - {\langle x\rangle}^{2}}$${SKEW} = {\frac{1}{N}{\sum\left\lbrack \frac{x - {\langle x\rangle}}{\sigma} \right\rbrack^{3}}}$${KURTOSIS} = {\frac{1}{N}{\sum\left\lbrack \frac{x - {\langle x\rangle}}{\sigma} \right\rbrack^{4}}}$${FIFTH} = {\frac{1}{N}{\sum\left\lbrack \frac{x - {\langle x\rangle}}{\sigma} \right\rbrack^{5}}}$

[0121] SKEW_REF, KURTOSIS_REF and FIFTH_REF

[0122] These look at the distribution, as above, in the reference imageinstead of the current image.

[0123] COMPACTNESS

[0124] This function looks at the four nearest pixels to each unmaskedpixel, and calculates the mean number of these that are unmasked.

[0125] OPACITY

[0126] Opacity is calculated, for the unmasked pixels only, as${OPACITY} = {\frac{1}{N}{\sum\left\lbrack \frac{x - x_{r}}{{\langle x\rangle} - x_{r}} \right\rbrack}}$

[0127] RUNNING_CORRELATION_MEAN

[0128] This is the standard deviation of the CORRELATION as definedabove. This is a running mean, as it is simply calculated from a set oftotal running sums.

[0129] RUNNING_MEAN_MEAN

[0130] This is the mean value of the masked correlation—as a runningvalue.

[0131] EDGE_EVIDENCE

[0132] This is based on a mask of particular edges in the image. Thismask is shrunk by one or two pixels all round. The unmasked pixels inthe current and reference images are examined using the EDGE algorithmabove. The routine then calculates the mean ratio of the pixels in theEDGE'd current image and those in the EDGE'd reference image, within theunmasked region, provided that the reference image contained a non-zerovalue.

[0133] PERCENTAGE_CHANGE

[0134] This is a measure of the percentage change in the number ofmasked pixels between the previous “filter” mask and the present one.These are Boolean masks, and the percentage change is calculated simplyon the basis of the numbers of pixels that are non-zero (TRUE) in justone of the two images, normalised by the number that are non-zero ineither or both.

[0135] The filter masks are “eroded” before this calculation, using analgorithm that only allows TRUE pixels to remain if all of its originalfour neighbours were also TRUE. This is a form of filtering to reducethe noise.

[0136] Rule-Based Analysis

[0137] Rule-based analysis is used initially to determine whether achange in the image has occurred, and whether this change issignificant. If it is, then further analysis is carried out to see ifthe change is considered to be associated with smoke, or whether it isassociated with, say, a person walking across the scene.

[0138] The rule-based analysis uses a scoring system, where points areallocated for each rule which is met. If the points total exceeds a(variable) criteria (typically 90% of the maximum score), the analysismoves to the next level.

[0139] The analysis is carried out on a region, which is a subset of thearea of the zone, defined by the edges of the unmasked pixels.

[0140] Check for No Correlation

[0141] If the running correlation for this zone is very small(RUNNING_CORRELATION_MEAN<0.1), this means that the reference image andthe current image are no longer similar (e.g. because the camera moved).If the image is not changing (PERCENTAGE_CHANGE<0.3), then it is time toupdate the zone's reference image, and abandon the current check forsmoke.

[0142] Correlation Less Than Threshold

[0143] If the correlation is less than the user-defined threshold, twopoints are scored, otherwise the check is abandoned.

[0144] Towards or From Common Mean

[0145] If the pixel values are tending towards the common mean, thenthis could indicate the presence of smoke (the whole image is becominguniform grey). The algorithm looks at the ratio of the towards to fromterms, and if this exceeds a user-adjustable ratio, three points arescored.

[0146] Edge-ness

[0147] The “edge-ness” of the region is the ratio of the EDGES to theCOUNT of pixels in the image. This is calculated both for the currentand the reference image. If the current image edge-ness is outside apreset band, three points are scored. An additional three points arescored if the edge-ness deviates from the reference edge-ness by morethan a preset percentage—selectably either up or down.

[0148] Compactness

[0149] The COMPACTNESS (defined above) must lie within a preset band. Ifit deviates outside of this, three points are scored.

[0150] Edge Evidence

[0151] The EDGE_EVIDENCE is decreased by the presence of smoke. If itfalls below a preset threshold, three points are scored.

[0152] Scoring against Criteria

[0153] The user may determine, when setting up the system, a subset ofthe available tests to carry out. The maximum score will be less, andthe is take into account when determining whether the score has exceeded90% of the maximum value. If it has, a Bayesian analysis is then carriedout.

[0154] Bayesian Analysis

[0155] Bayesian analysis provides us with a well founded decisioncriteria which takes into account the co-variance of features andprovides the ability to discriminate between different classes of event(nuisance and real alarms). An important fact to note when definingfeatures for use with Bayesian analysis is that they should be invariantto external influences such as background and lighting. The algorithmcan cope with some variation but in general the effects of externalinfluences should be kept to a minimum.

[0156] Bayesian statistics are a useful tool in making decisions withmultivariate systems such as this. The parameters (MEAN,TOWARDS_COMMON_MEAN etc) are combined together into an n-dimensionalvector. These vectors are used to “train” the system by building up aset of statistics. More specifically, the system stores data fornuisance and real alarms as separate classes. For an n-dimensionalvector v the sums s and S are calculated for N different alarm events asfollows, separately for nuisance and real alarms.

s=Σv

S=Σvv ^(T)

[0157] The Bayesian decision function takes a vector, v, from thecurrent zone/region, and calculates a real decision value, d, asfollows: $m = \frac{s}{N}$ $C = {\frac{S}{N} - {mm}^{T}}$d = 0.5 × (log C + (v − m)^(T)C⁻¹ ⋅ (v − m))

[0158] d is calculated against the two reference classes—nuisance andreal, giving d_(n) and d_(r) If d_(r) is greater than d_(n), theBayesian analysis signals an alarm condition.

[0159] If problems are experienced with overlapping responses in d_(n)and d_(r), this might be solved by increasing the number of features andhence moving a to higher dimensional spaces (the probability of cloudsoverlapping by chance reduces as the dimensionality is increased).

[0160] Combination of Rules and Bayesian Analysis

[0161] It is crucial that the smoke detection system avoids falsealarms. This is a key part of the system.

[0162] Thus an important feature of the invention is to combine arule-based analysis with a atatistically based analysis, andparticularly with one based on Bayesian analysis. The rule basedanalysis takes place first and if certain criteria are met then theBayesian analysis is instigated.

[0163] Frequently, the Bayesian analysis and the rule-based analysisdisagree. In this case, the confidence in the Bayesian analysis is usedto determine whether the alarm is real or nuisance. The differencebetween real and nuisance is based on experience and the system buildsup in accuracy over time.

[0164] If the Bayesian analysis showed an alarm, but the rule-basedanalysis did not, The difference between the values of d_(r) and d_(n)is used as a measure of the confidence in the alarm. If this exceeds theminimum confidence level, then an alarm is signalled, even though therule-based analysis did not trigger and alarm

[0165] If the rule based analysis showed an alarm, and the Bayesiantreatment did not, if the difference between d_(n) and d_(r) is morethan the minimum confidence level, the alarm is cancelled.

[0166] If there is no alarm, but the correlation between the current andreference images is small, and the percentage change function is low,the reference image is updated. This effectively adjusts for changes in,for example, lighting level.

1. A smoke detection system comprising video camera monitoring means,video frame comparison means, signal processing means, and alarmgenerating means dependent on an output from the signal processingmeans; the signal processing means being arranged to analyse successiveframes acquired by the video camera monitoring means and to compare theintensity and/or colour of individual pixels or group of pixels so as toconsider the overall characteristics and inter-relationships of thesepixels so as to detect the presence of smoke characterised in that thesignal processing means analyses a plurality of different types ofchange in the said characteristics and inter-relationships.
 2. A smokedetection system according to claim 1 in which the signal processingmeans is arranged to analyse the changes by a combination of a weightedrule based analysis and a statistically based analysis.
 3. A smokedetection system according to claim 2 in which the statistically basedanalysis is a Bayesian analysis.
 4. A smoke detection system accordingto claim 1 in which a change to be analysed is that the value of atleast some of the pixels being analysed are identified as convergingtowards a mean value.
 5. A smoke detection system according to claim 1in which a change to be analysed is that pixels defining edgeinformation change from showing structured definition to showing a lessstructured level of information.
 6. A smoke detection system accordingto claim 1 in which a change to be analysed is that dynamic parts of theoverall image are identified as becoming static.
 7. A smoke detectionsystem according to claim 1 in which a change to be analysed is thatmeasured differences are identified as being distributed in smallisolated groups.
 8. A smoke detection system in which a change to beanalysed is that the value of changed pixels as between a current imageand a reference image indicates a reduction in visibility.
 9. A smokedetection system according to claim 1 in which a change to be analysedis that new regions are identified as appearing which havecharacteristics of shape which resemble those of known shapecharacteristics of an emerging smoke or fire condition.
 10. A smokedetection system according to claim 1 or 2 in which a plurality of thechanges in accordance with at least any two of claims 4 to 9 is arrangedto be analysed.
 11. A smoke detection system according to claim 4 inwhich a number of frames are arranged to be analysed and the value ofeach pixel or pixel group is examined to see whether it is converging toa mean value.
 12. A smoke detection system according to claim 1 in whichthe signal processing means is arranged to analyse four different typesof change, the first change being that the value of at least some of thepixels within the region are identified as converging towards a meanvalue, the second change being that pixels defining edge informationchange from showing structured definition to showing a less structuredlevel of information, the third change being that the overall imageceases to be static and is identified as moving in an unpredictablemanner and the fourth change being that new regions are identified asappearing which have shaped characteristics which resemble those ofknown shaped characteristics of an emerging smoke or fire condition.